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Description

As the applications of data mining, the non-trivial extraction of implicit information in a data set, have expanded in recent years, so has the need for techniques that are tolerable to imprecision, uncertainty, and approximation.

Intelligent Soft Computation and Evolving Data Mining: Integrating Advanced Technologies is a compendium that addresses this need. It integrates contrasting techniques of conventional hard computing and soft computing to exploit the tolerance for imprecision, uncertainty, partial truth, and approximation to achieve tractability, robustness and low-cost solution. This book provides a reference to researchers, practitioners, and students in both soft computing and data mining communities, forming a foundation for the development of the field.

Topics Covered

Artificial Clonal Selection

Biomedical data mining

Cat Swarm Optimization

Computational Intelligence

Data Mining

Fuzzy Functional Dependencies

Fuzzy Neural Network

Hybrid Evolutionary Learning Algorithms

Power System Load Frequency Control using

Risk-Management Models

Reviews and Testimonials

While integrating advanced technologies clearly falls in the emerging category because of recency, it is now beginning to reach popularity and more books on this topic becomes desirable. It is hoped that this book will provide a reference to researchers, practitioners, students in both soft computing and data mining communities and others, for the benefit of more creative ideas.

Preface

Since its inception, data mining has been described as "the nontrivial extraction of implicit, previously unknown, and potentially useful information from data". It was usually used by business intelligence organizations and analysts to extract useful information from databases. But increasing applications of data mining have been found in other areas to extract information from the enormous data sets generated by modern experimental and observational methods. However, due to the intractable computational complexity of many existing data mining techniques for real world problems, techniques that are tolerable to imprecision, uncertainty, and approximation are very desirable.

In contrast to conventional hard computing, the basic ideas underlying soft computing is to exploit the tolerance for imprecision, uncertainty, partial truth, and approximation to achieve tractability, robustness and low solution cost. At this juncture, the principal constituents of soft computing are Fuzzy Logic (FL), Neural Computing (NC), Evolutionary Computation (EC), Genetic Algorithms (GA), Swam Intelligence (SI), Machine Learning (ML) and Probabilistic Reasoning (PR), with the latter subsuming belief networks, chaos theory and parts of learning theory. It has been demonstrated in many areas that the soft computing methodologies are complementary to many existing theories and technologies.

As such, the objective of this book is to present an international forum for the synergy of new developments from two different research disciplines. It is hoped that through the fusion of diverse techniques and applications, new and innovative ideas will be stimulated and shared.

This book contains nineteen chapters written by leading experts from researchers of soft computing and data mining communities as well as practitioners from medical science, space and geo-information science, innovative life science, and traffic and transportation engineering. The book is organized into three sections. The first section shows four innovative works that give a flavor of how soft computation and data mining can be integrated for various applications. The second section compiles nine new soft computation techniques for different real world applications, with a leading chapter of survey to classify current computational intelligence technologies. The third section is devoted to five real life problems that can be addressed by the proposed new data mining techniques. Since the chapters are written by many researchers with different backgrounds around the world, the topics and content covered in this book provides insights which are not easily accessible otherwise.

While integrating advanced technologies clearly falls in the emerging category because of recency, it is now beginning to reach popularity and more books on this topic becomes desirable. It is hoped that this book will provide a reference to researchers, practitioners, students in both soft computing and data mining communities and others, for the benefit of more creative ideas.

We are grateful to all authors for their contributions and the referees for their vision and efforts. We would like to express our thanks to IGI Global and National University of Kaohsiung for realizing this book project.

Author(s)/Editor(s) Biography

Leon Shyue-Liang Wang received his Ph.D. from State University of New York at Stony Brook in 1984. From 1984 to 1987, he joined the University of New Haven as assistant professor. From 1987 to 1994, he joined New York Institute of Technology as assistant/associate professor. From 1994 to 2002, he joined I-Shou University in Taiwan and served as Director of Computing Center, Director of Library, and Chairman of Information Management Department. In 2002, he joined National University of Kaohsiung, Taiwan. In 2003, he rejoined NYIT. He is now professor and chairman in National University of Kaohsiung, Taiwan. He has published over 150 papers in the areas of data mining and soft computing, and served as a PC member of several national and international conferences. He is a member of the board of Chinese American Academic and Professional Society, USA.

Tzung-Pei Hong received his B.S. degree in chemical engineering from National Taiwan University in 1985, and his Ph.D. degree in computer science and information engineering from National Chiao-Tung University in 1992. He was a faculty at the Department of Computer Science in Chung-Hua Polytechnic Institute from 1992 to 1994, and at the Department of Information Management in I-Shou University from 1994 to 2001. Since 2001, he has served as Director of Library and Computing Center, Dean of Academic Affair, and Vice President of National University of Kaohsiung. He is currently a distinguished professor at the Department of Electrical Engineering and the department of Computer Science and Information Engineering in National University of Kaohsiung. His current research interests include machine learning, data mining, soft computing, management information systems, WWW applications and has published more than 300 technical papers.